{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "953ae391",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torch.nn.modules.container.Sequential'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.1623, -0.0576, -0.2746,  0.1790, -0.2320, -0.1048, -0.0170, -0.0088,\n",
       "         -0.0498, -0.1627],\n",
       "        [-0.0687,  0.0394, -0.2942,  0.1759, -0.2788, -0.1162,  0.0035,  0.0612,\n",
       "         -0.0477, -0.3413]], grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "\n",
    "net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))\n",
    "print(type(net))\n",
    "X = torch.rand(2, 20)\n",
    "# net(X)\n",
    "net.__call__(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d9018c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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